DCNN and LDA-RF-RFE Based Short-Term Electricity Load and Price Forecasting

Abstract

In this paper, Deep Convolutional Neural Network (DCNN) is proposed for short term electricity load and price forecasting. Extracting useful information from data and then using that information for prediction is a challenging task. This paper presents a model consisting of two stages; feature engineering and prediction. Feature engineering comprises of Feature Extraction (FE) and Feature Selection (FS). For FS, this paper proposes a technique that is combination of Random Forest (RF) and Recursive Feature Elimination (RFE). The proposed technique is used for feature redundancy removal and dimensionality reduction. After finding the useful features DCNN is used for electricity price and load forecasting. DCNN performance is compared with Convolutional Neural Network (CNN) and Support Vector Classifier (SVC) models. Using the forecasting models day-ahead and the week ahead forecasting is done for electricity price and load. To evaluate the CNN, SVC and DCNN models, real electricity market data is used. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the models. DCNN outperforms compared models by yielding lesser errors.